Foundation Models and Self-Supervised Adaptation for Remote Sensing Registration and Fusion

Dear Colleagues,

We are pleased to announce the launch of this second edition of our Special Issue of Remote Sensing entitled: Foundation Models and Self-Supervised Adaptation for Remote Sensing Registration and Fusion. This Special Issue follows in the footsteps of the first edition, “Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection” (https://www.mdpi.com/journal/remotesensing/special_issues/972O6124W3).

Multi-temporal and multi-modal remote sensing image analysis involves data acquired by different sensors, at different times, and from different viewpoints, with registration and fusion being fundamental to aligning and integrating such heterogeneous observations. The core challenge lies in achieving precise registration and efficient fusion under radiometric and geometric differences, limited annotation, and domain shifts, so that reliable surface information can be derived for applications such as disaster assessment, urban planning, and environmental monitoring.

This Special Issue aims to gather together the latest breakthroughs in foundation models and self-supervised adaptation for remote sensing registration and fusion. It will promote the development of a new generation of intelligent processing algorithms that can address the challenges posed by massive, multi-source, and heterogeneous remote sensing data. The goal is to enhance the automation and intelligent interpretation capabilities of Earth observation systems and to provide precise decision-making support for resource monitoring, urban planning, and sustainable development.

This Special Issue welcomes original research and review articles covering areas of interest that include, but are not limited to, the following topics:

1. Image Registration: Foundation model-based feature extraction and matching; parameter-efficient and self-supervised adaptation; non-rigid registration; cross-modal and cross-temporal image registration; unsupervised and weakly supervised registration.
2. Image Fusion: Pansharpening; multi-modal fusion (e.g., optical-SAR); spatio-temporal fusion; generative model-based fusion; foundation model adaptation and self-supervised learning for fusion.
3. Self-Supervised Adaptation for Registration and Fusion: Pre-training and transfer learning; contrastive and masked image modeling; cross-modal consistency; domain adaptation; prompt tuning, adapters, and other parameter-efficient fine-tuning strategies.

Dr. Shikai Jiang
Dr. Yuanxin Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.